library(analysistools)
library(dplyr)
my_data <- analysistools::analysistools_MSNA_template_data
sampling_frame <- data.frame(
strata = c("admin1a", "admin1b", "admin1c"),
population = c(100000, 200000, 300000)
)
my_data <- my_data %>%
add_weights(sampling_frame, "admin1", "strata", "population")
my_loa <- analysistools::analysistools_MSNA_template_loa
my_design <- srvyr::as_survey_design(my_data, weights = "weights", strata = "admin1")
my_results <- create_analysis(my_design, loa = my_loa, sm_separator = "/")04 - R framework with IMPACT - session 4
Recap
Outputs - Wide tables
library(presentresults)
my_results_table <- my_results$results_tableThe framework is built around 4 steps: cleaning, composition, analysis, outputs
- Cleaning: Any manipulation to go from the raw data to the clean data
- Composition: Any manipulation before the analysis e.g. adding indicators, adding information from loop, main dataset, or any other dataset (e.g. previous round), aok aggregation, etc.
- Analysis: Any manipulation regarding only the analysis.
- Outputs: Any manipulation to format the outputs. Outputs are created from the results table, from the stat + analysis key
The following section will present some introduction about the outputs.
There are currently two types of table:
- one that have the variables in the rows and the disagregation in the columns,
- one that have the disagregation in the rows and the variables in the columns.
There are two steps to turn a results table:
- Turn the long results table to a large results table.
- Format and export it to Excel.
create_*_group_x_variable
Wide table with the groups in the rows and the variables in the columns.
my_results_table %>%
create_table_group_x_variable() %>%
create_xlsx_group_x_variable(file_path = "outputs/04 - example - group_x_variable.xlsx", overwrite = T)create_*_variable_x_group
Wide table with the variables in the rows and the groups in the columns.
my_results_table %>%
create_table_variable_x_group() %>%
create_xlsx_variable_x_group(file_path = "outputs/05 - example - variable_x_group.xlsx", overwrite = T)Tabular HTML
The folders 05 - reach_tabular_html_* are example of Quarto projects. They can be used to produce some tables in the html format.
The params in the header can be change. Use Render after to create the html output.
Note
The following is work in progress. It will later become something like create_html_variable_x_group.
Exercise
Exercise 1
- Create an excel table with the strata in the rows and the variables in the columns.
library(presentresults)exercise_outputs <- readxl::read_excel("inputs/10 - exercise - analysis_to_review.xlsx")Exercise 2
- Try the tabular html output.
- Try to edit the authors, RCID and the introduction.